Communication and computing evolution have brought to reality new paradigms, such as the Internet-of-Things (IoT), generating massive amounts of decentralized and heterogeneous data. This trend has given machine and deep learning (ML/DL) techniques huge relevance in our current society since data quantity and quality are essential requirements to operate successfully. However, with stakeholders becoming more aware of how their data is used and data privacy finding its way into the mainstream debate, a new approach to ML/DL was required to appease these concerns. Introduced by Google in 2016 [1]}, federated learning (FL) emerged as a possible solution. FL is a paradigm that enables participants to train an ML/DL model collaboratively, and most importantly, it does so without having to share the participants' datasets. The paradigm provides three different scenarios according to how data is distributed between participants. In horizontal federated learning (HFL), participants (also known as clients) have the same feature space but different samples [2]}. In the vertical federated learning (VFL) scenario, participants hold different features, but the same samples [3]}. Finally, when neither feature space nor samples are the same between participants, the scenario is termed federated transfer learning (FTL). In recent years, heterogeneous use cases suitable for the previous three scenarios, especially for HFL, have provided levels of performance comparable to classical ML/DL algorithms where data privacy is not considered [4]}. Furthermore, since the datasets never leave the participants' possession in the FL paradigm, the logistical problems of aggregating, storing, and maintaining data in central silos are eliminated.
